What is the cost to add AI features to SaaS?
The cost to add AI features to SaaS has 4 parts: building it, paying for model usage, testing and monitoring it, and keeping it working. Most teams underestimate the last 2.
Build cost is usually the largest part up front. Model usage is often small at first but grows with adoption. Evals and upkeep continue for as long as the feature ships.
Know all 4 before you commit. A cheap prototype can hide a large monthly bill, and a cheap model bill can hide weeks of engineering time.
Build cost: people and time
Build cost is the time of the people who design, build and ship the feature. It depends on the feature, not the model.
A summary or draft feature on data you already have is small. Search across customer data adds retrieval, permissions and tests. An agent that takes actions adds tool design, limits and review.
Typical market ranges we have seen: a studio often charges $15,000 to $60,000 to add one AI feature to an existing SaaS product. Production-ready prototype work starts from about $5,000.
AI MVP development cost follows the same logic. An MVP (minimum viable product) needs less polish, but it still needs evals and data handling if real customers will use it.
These factors push build cost up or down:
- Data access: clean, well-structured data is cheap to use. Data spread across old systems is not.
- Permissions: multi-tenant products need filters so one customer never sees another's data.
- Risk: features that act on the user's behalf need limits, logs and human approval.
- UI (user interface) work: showing sources, letting users edit drafts and collecting feedback all take time.
- Team experience: a team that has shipped AI features before avoids many false starts.
How does model usage pricing work?
Hosted model providers charge per token. A token is a small chunk of text. Anthropic says 1 token is roughly 4 characters or 0.75 words in English.
Input tokens (what you send) and output tokens (what the model writes) are priced separately. Output usually costs more per token. Prices are quoted per million tokens.
As of September 25, 2026, Anthropic's API pricing page lists Claude Haiku 4.5 at $1 per million input tokens and $5 per million output tokens. Larger models cost more.
Providers also offer discounts. Anthropic lists a 50% discount for batch requests that can wait, and cheaper reads of cached prompt text. OpenAI's API pricing page shows separate batch and cached input prices too. Check both pages before you budget, since prices change.
- Cost per request = input tokens x input price + output tokens x output price.
- Monthly cost = cost per request x requests per user x active users.
- Retrieval and agents add input tokens, because each call carries more context.
- Agents cost more per task, because one task can mean many model calls.
- Smaller models cost less. Use a larger one only where your evals show it is needed.
Evals and monitoring: the cost most teams skip
An eval is a set of test cases with a way to score the model's answers. You run it before every prompt or model change.
Budget time to collect and label examples, write the scoring, and review failures. Evals that use a model to grade answers also add usage cost.
Monitoring means tracking errors, slow requests, cost per customer and user feedback in production. It needs dashboards and alerts, and someone who looks at them.
Skipping this work does not save money. It moves the cost to later, when a bad answer reaches a customer or a bill spikes without warning.
A simple rule: plan for evals and monitoring to take real time in every release, not just the first one.
Maintenance: what it costs to keep AI features working
AI features need upkeep that normal features do not. Providers release new models and retire old ones. Your data and your users' questions change too.
- Re-run evals and adjust prompts when you change models.
- Review a sample of real outputs each week or month.
- Fix failure cases your users report, then add them to the eval.
- Watch cost per active user as adoption grows.
- Keep security reviews current as you add tools or data sources.
A worked example with hypothetical numbers
These numbers are made up and rounded to show the method. They are not a quote. Replace each one with your own.
Say a SaaS company with 2,000 active users adds a feature that drafts replies from a customer's records. Each draft sends about 4,000 input tokens and returns about 500 output tokens.
Assume a model priced at $1 per million input tokens and $5 per million output tokens. One draft then costs about $0.004 for input and $0.0025 for output, or about $0.0065.
If each user makes 50 drafts a month, that is 100,000 drafts. The model bill is about $650 a month.
- In this example, year 1 costs about $30,000 to build plus about $50,000 to run and maintain.
- The model bill is under a sixth of the ongoing cost. People time is the rest.
| Cost part | Hypothetical amount | Timing | |
|---|---|---|---|
| Build (one engineer for 6 weeks plus review) | $30,000 | One time | |
| Model usage at 100 | 000 drafts | $650 a month | Ongoing |
| Evals and monitoring upkeep | $1,500 a month | Ongoing | |
| Maintenance and model changes | $2,000 a month | Ongoing |
Build vs buy AI features: which costs less?
Buy when the feature is not core to your product and a vendor already does it well. Build when the feature uses your own data in a way no vendor can reach.
- Buy: generic tasks, such as meeting notes or internal writing help.
- Build: features inside your product that use your customers' data.
- Blend: build the product logic, but use hosted models rather than training your own.
- Count vendor per-seat fees against your own build and upkeep costs over 2 years.
How much does AI automation and AI consultant help cost?
How much AI automation costs depends on the same 4 parts. Internal automations are often cheaper to build because fewer users see them.
AI consultant cost varies by model. Typical market ranges we have seen: a fractional Head of AI costs roughly $4,000 to $30,000 a month. A fractional CTO (chief technology officer) costs $2,000 to $25,000 a month.
Advisory help pays off when it saves wasted build time. A senior advisor can often tell you early which feature to cut, which model tier is enough, and what to test.
Valley AI publishes its rates. Starter is 5 hours a week for $3,500 a month. Growth is 10 hours for $6,500, and Scale is 20 hours for $11,500.
Seeour pricing or how we add AI features to SaaS products.